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Scaling Data Science at Airbnb

Delivered by Elena Grewal at the 2016 New York R Conference on April 8th and 9th at Work-Bench.

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Scaling Data Science
Structure + tooling at Airbnb
Elena Grewal / April 9, 2016 / @elenatej
I started at Airbnb ~4 years ago
● Ph.D. in Education
● Team grown from 5 to 70
○ Fun fact for this conference:
■ 54% mostly use R, 29% mostly use Python
■ 80% very comfortable with R, 50% with Python
● Company grown from 200 to 2,500
Scaling Data Science at Airbnb
Scaling
● Team growth
● Interview process
● Why diversity
● Using data
○ Top of funnel changes
○ Conversion changes
● Scaling diversity data science
Rapid Data Team Growth
Diversity was important to us, but it wasn’t happening
Women :(
Team
Size
Interview process focused on practical data skills
‘Data challenges’ - Airbnb data + real question
Multi-stage
- Recruiter screen
- Take home data challenge
- Onsite challenge
- 1:1s with hiring manager, business partner, CV
We felt good about the data challenges and process
● Popular Quora
post
● Process starts
being used by
other
companies (!)
Why act now
● Harder to hire women as ratio declines
● Women could feel excluded on team
● Homogeneity -> narrower range of ideas
We believe in a world where
people belong, anywhere.
We started by looking at the data.
-
Scaling Data Science at Airbnb
● Manual audit of past apps
● EEOC data on inbound
applicants
FUNNEL
IMAGE
30% women
No drop off
Drop off
Drop off
We then thought about everything we
could possibly do to make a difference
And we did those things.
FUNNEL
IMAGE
Lightning talks
Support community
Diversity on multiple
dimensions
Encourage applicants
Blog Posts & Interviews
Highlight Women @
Airbnb
Inspire women in
data more broadly
Women in data dinners
Create
community of
senior women
in field
Circulates to
multiple
companies
(not just
Airbnb)
Scaling Data Science at Airbnb
● Create standard rubric
● Binary scoring system
● Removed names for a bit
● Trained graders
● Two graders for each test
to ensure consistency
● “Buddy” coffee chat &
support
● 50% women at presentation
● Clearer success criteria
Increase in Female Hires
High employee satisfaction scores + 100% women belong
Scaling Data Science at Airbnb
Our work is not complete.
Next steps
● Focus on multiple aspects of diversity
○ Apply similar process to thinking about racial diversity
○ Other dimensions as well
○ Continue to improve interview process for all - stay vigilant
● Continue to monitor team culture and belonging of current
employees
● Help the rest of the company and scale the efforts
Scaling our efforts
What about the rest of Airbnb?
● Full time data scientist + data engineer to work with our
“People and culture team”
● AWS account held separate from main Airbnb data
● Built tool to request and collect diversity data from referrals
and passively sourced candidates
● Dashboards with diversity data for every team
Thank you!

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Scaling Data Science at Airbnb

  • 1. Scaling Data Science Structure + tooling at Airbnb Elena Grewal / April 9, 2016 / @elenatej
  • 2. I started at Airbnb ~4 years ago ● Ph.D. in Education ● Team grown from 5 to 70 ○ Fun fact for this conference: ■ 54% mostly use R, 29% mostly use Python ■ 80% very comfortable with R, 50% with Python ● Company grown from 200 to 2,500
  • 4. Scaling ● Team growth ● Interview process ● Why diversity ● Using data ○ Top of funnel changes ○ Conversion changes ● Scaling diversity data science
  • 6. Diversity was important to us, but it wasn’t happening Women :( Team Size
  • 7. Interview process focused on practical data skills ‘Data challenges’ - Airbnb data + real question Multi-stage - Recruiter screen - Take home data challenge - Onsite challenge - 1:1s with hiring manager, business partner, CV
  • 8. We felt good about the data challenges and process ● Popular Quora post ● Process starts being used by other companies (!)
  • 9. Why act now ● Harder to hire women as ratio declines ● Women could feel excluded on team ● Homogeneity -> narrower range of ideas
  • 10. We believe in a world where people belong, anywhere.
  • 11. We started by looking at the data. -
  • 13. ● Manual audit of past apps ● EEOC data on inbound applicants
  • 14. FUNNEL IMAGE 30% women No drop off Drop off Drop off
  • 15. We then thought about everything we could possibly do to make a difference And we did those things.
  • 17. Lightning talks Support community Diversity on multiple dimensions Encourage applicants
  • 18. Blog Posts & Interviews Highlight Women @ Airbnb Inspire women in data more broadly
  • 19. Women in data dinners Create community of senior women in field Circulates to multiple companies (not just Airbnb)
  • 21. ● Create standard rubric ● Binary scoring system ● Removed names for a bit ● Trained graders ● Two graders for each test to ensure consistency
  • 22. ● “Buddy” coffee chat & support ● 50% women at presentation ● Clearer success criteria
  • 23. Increase in Female Hires High employee satisfaction scores + 100% women belong
  • 25. Our work is not complete.
  • 26. Next steps ● Focus on multiple aspects of diversity ○ Apply similar process to thinking about racial diversity ○ Other dimensions as well ○ Continue to improve interview process for all - stay vigilant ● Continue to monitor team culture and belonging of current employees ● Help the rest of the company and scale the efforts
  • 27. Scaling our efforts What about the rest of Airbnb? ● Full time data scientist + data engineer to work with our “People and culture team” ● AWS account held separate from main Airbnb data ● Built tool to request and collect diversity data from referrals and passively sourced candidates ● Dashboards with diversity data for every team